提出了一种基于函数联接的感知器神经网络的纹理分类方法.它采用高斯-马尔柯夫随机场模型(GMRF)对纹理进行描述,模型参数即为纹理特征,参数估计采用最小平方误差方法获得.将估计参数作为表达纹理的特征向量,用感知器网络对特征进行分类,并且采用函数联接的方式解决线性不可分问题.对纹理图象进行的实验表明,采用这种方法能够提高学习速度,简化计算过程,并取得较好的纹理分类效果.

This paper presents a texture classification approach based on function link network. Image texture is characterized by the second order Gauss MRF model, and the least square error estimation is employed for the estimation of model parameters. However, these parameters are proved to be inefficient in texture classification. To solve this problem, we introduced a function link network to improve the classification performance. Experiment shows that better classification results can be obtained than traditional euclidean distance approach, and it has the advantage of simple processing procedure and fast convergence speed.